Readout for simple and precise analog acoustic impact initialization

An economic concept of acoustic shock wave sensing readout system for simple computer processing is introduced in this work. Its application can be found in precise initialization of the stopwatch from the starter sound, handclap or gun in competitive sport races but also in many other places. The proposed device consists of several low-cost commercially available components and it is powered by a 9 V battery. The proposed device reliably reacts on incoming acoustic shock wave by generation of explicit impulse having controllable duration. It significantly overcomes basic implementations using only a microphone and amplifier (generating parasitic burst instead of defined and distinct impulse) or systems allowing a limited number of adjustable features (gain and/or threshold of the comparator—our concept offers the adjustment of gain, cut-off frequency, threshold level and time duration of active state). In comparison with standard methods, the proposed approach simplifies and makes sensing device less expensive and universal for any powder-based starting gun (without necessity to adapt starting gun). The proposed device, among others, has the following features: impulse duration can be controlled from hundreds of μs up to 2.3 s, the gain range of linear part of processing from 6 to 40 dB and open-collector output compatible with 5 V TTL or 3.3 V CMOS logic. The initialization has been tested in the range from tens of centimeters up to four meters. In order to highlight the important spectral components, the spectral character of the signal can be optimally reduced by a low-pass filter. The quiescent power consumption of the designed simple analog circuit reaches 90 mW. Several use cases, response of the designed system on gunshot signature, talking, hand-clapping and hit on the sensing microphone, are studied and compared to each other. Simulation and experimental results confirm functionality of the realized system.

Pulsating Flow Past a Square Cylinder: Analysis of Force Coefficient Spectra and Vortex Structure Development

This study examines the changes in force coefficients and wake flow structures of a square cylinder subject to pulsating in-flow at different frequencies. The Reynolds number is 200, according to previous literature. Over a range of forcing frequencies, a regime is observed where the shedding frequency scales with the forcing frequency rather than the natural shedding frequency, known as the lock-in phenomenon in literature. The change in spectral character across three frequency regimes—pre-lock-in, lock-in, and post-lock-in—are examined and characterized. During pre-lock-in, the shedding frequency remains equal to the natural shedding frequency. However, the corresponding peak in lift coefficient (CL) power spectral density (PSD) is a single decade larger than that of neighboring minima. This contrasts greatly with subsequent regimes where the amplitudes of the peaks are observed to be substantially larger than the amplitudes of neighboring minima. The onset of lock-in is sharp, and the corresponding excitation frequency is identified. The shedding frequency becomes a function of the forcing frequency within this regime, and the corresponding CL PSD peak is four decades larger than that of neighboring minima. The transition beyond the lock-in regime is gradual with peaks of the spectra broadening until separating into multiple discrete peaks. To comprehend the changes in the force coefficients, the vortex structure in the wake is characterized at different frequencies. The connection between the vortex development sequence and force profile is investigated, and z-vorticity probes are utilized to correlate these qualitative observations with prior quantitative analysis. Three-dimensional flow effects are also examined.

A simple approach to nonstationary source waveform estimation in seismic records

A simple and practical method for source-waveform estimation from reflection seismic records is implemented by iterative identification of locally strongest reflections. Instead of conventional hypotheses about statistical properties of the whole records, the method is based on a general observation that stronger reflection peaks occur relatively sparsely and that smaller peaks adjacent to them are mutually incoherent. Tests with real well logs suggest that the subsurface often possesses such sparseness. Based on this property, the source waveform is obtained from seismic records by optimizing a combination of its practically important properties, such as the main-lobe width, side-lobe amplitudes, and phase character. Similarly, other types of optimization criteria can be used. The approach is stable with respect to noise and parameter variations and allows estimating the source waveforms without well-log control. By including inverse [Formula: see text]-filtering, time-variant amplitude scaling, and/or band-pass filtering, the approach allows correcting for reflection amplitude variations, nonstationarity due to seismic attenuation and dispersion, and also for coherent noise consisting in possible amplitude variations and phase shifts of the low- or high-frequency components of the records. By using the estimated source waveform, time-dependent waveforms and nonstationary wavelet matrices can be predicted at any reflection time. The method is illustrated by synthetic examples of logs with von Kármán distributions of velocity fluctuations and real well logs from an oil reservoir. In the synthetics, the spectral character and shape of the main lobe of the source waveform are reproduced well in all cases, and the phase character of the source is partly recovered when the subsurface reflectivity is dominated by reflections that are relatively sparse and strong compared to the adjacent reflectivity. In cases in which the phase of the source signal is unknown, the obtained waveforms are characterized by simple shapes with low-amplitude side lobes. Such waveforms are suitable for many applications from well ties and numerical modeling to deconvolution and [Formula: see text]-compensation.

How large are temporal representativeness errors in paleoclimatology?

Ongoing work in paleoclimate reconstruction prioritizes understanding the origins and magnitudes of errors that arise when comparing models and data. One class of such errors arises from assumptions of proxy temporal representativeness (TR), i.e., how accurately proxy measurements represent climate variables at particular times and time intervals. Here we consider effects arising when (1) the time interval over which the data average and the climate interval of interest have different durations, (2) those intervals are offset from one another in time (including when those offsets are unknown due to chronological uncertainty), and (3) the paleoclimate archive has been smoothed in time prior to sampling. Because all proxy measurements are time averages of one sort or another and it is challenging to tailor proxy measurements to precise time intervals, such errors are expected to be common in model–data and data–data comparisons, but how large and prevalent they are is unclear. This work provides a 1st-order quantification of temporal representativity errors and studies the interacting effects of sampling procedures, archive smoothing, chronological offsets and errors (e.g., arising from radiocarbon dating), and the spectral character of the climate process being sampled. Experiments with paleoclimate observations and synthetic time series reveal that TR errors can be large relative to paleoclimate signals of interest, particularly when the time duration sampled by observations is very large or small relative to the target time duration. Archive smoothing can reduce sampling errors by acting as an anti-aliasing filter but destroys high-frequency climate information. The contribution from stochastic chronological errors is qualitatively similar to that when an observation has a fixed time offset from the target. An extension of the approach to paleoclimate time series, which are sequences of time-average values, shows that measurement intervals shorter than the spacing between samples lead to errors, absent compensating effects from archive smoothing. Nonstationarity in time series, sampling procedures, and archive smoothing can lead to changes in TR errors in time. Including these sources of uncertainty will improve accuracy in model–data comparisons and data comparisons and syntheses. Moreover, because sampling procedures emerge as important parameters in uncertainty quantification, reporting salient information about how records are processed and assessments of archive smoothing and chronological uncertainties alongside published data is important to be able to use records to their maximum potential in paleoclimate reconstruction and data assimilation.

How large are temporal representativeness errors in paleoclimatology?

Ongoing work in paleoclimate reconstruction prioritizes understanding the origins and magnitudes of errors that arise when comparing models and data. One class of such errors arises from assumptions of proxy temporal representativeness – broadly, the time scales over which paleoclimate proxy measurements are associated with climate variables. In the case of estimating time mean values over an interval, errors can arise when the time interval over which data are averaged and the interval that is being studied have different lengths, or if those intervals are offset from one another in time. Because it is challenging to tailor proxy measurements to precise time intervals, such errors are expected to be common in model-data and data-data comparisons, but how large and prevalent they are is unclear. The goal of this work is to provide a framework for first-order quantification of temporal representativity errors and to study the interacting effects of sampling error, archive smoothing (e.g. by bioturbation in sediment cores), chronological offsets and errors (e.g. arising from radiocarbon dating), and the spectral character of the climate process being sampled. In some cases, particularly for small values of target intervals τx relative to sample intervals τy, errors can be large relative to signals of interest. Errors from mismatches in τx and τy can have magnitudes comparable to those from chronological uncertainty. Archive smoothing can reduce sampling errors by acting as an anti-aliasing filter, but destroys high-frequency climate information. An extension of the approach to paleoclimate time series, which are sequences of time-average values, shows that measurement intervals shorter than the spacing between samples lead to errors, absent compensating effects from archive smoothing. Including these sources of uncertainty will improve accuracy in model-data comparisons and data comparisions and syntheses. Moreover, because sampling procedures emerge as important parameters in uncertainty quantification, reporting salient information about how records are processed and assessments of archive smoothing and chronological uncertainties alongside published data is important to be able to use records to their maximum potential in paleoclimate reconstruction and data assimilation.

RESEARCH OF REGISTRATION APPROACHES OF THERMAL INFRARED IMAGES AND INTENSITY IMAGES OF POINT CLOUD

In order to realize the analysis of thermal energy of the objects in 3D vision, the registration approach of thermal infrared images and TLS (Terrestrial Laser Scanner) point cloud was studied. The original data was pre-processed. For the sake of making the scale and brightness contrast of the two kinds of data meet the needs of basic matching, the intensity image of point cloud was produced and projected to spherical coordinate system, histogram equalization processing was done for thermal infrared image.This paper focused on the research of registration approaches of thermal infrared images and intensity images of point cloud based on SIFT，EOH-SIFT and PIIFD operators. The latter of which is usually used for medical image matching with different spectral character. The comparison results of the experiments showed that PIIFD operator got much more accurate feature point correspondences compared to SIFT and EOH-SIFT operators. The thermal infrared image and intensity image also have ideal overlap results by quadratic polynomial transformation. Therefore, PIIFD can be used as the basic operator for the registration of thermal infrared images and intensity images, and the operator can also be further improved by incorporating the iteration method.